基于 LLM 的数字人何时能可靠替代人类问卷受访者？

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Abstract:Digital personas powered by Large Language Models (LLMs) are increasingly proposed as substitutes for human survey respondents, yet it remains unclear when they can reliably approximate human survey findings. We answer this question using the LISS panel, constructing personas from respondents' background variables and pre-2023 survey histories, then testing them against the same respondents' held-out post-cutoff answers. Across four persona architectures, three LLMs, and two prediction tasks, we assess performance at the question, respondent, distributional, equity, and clustering levels. Digital personas improve alignment with human response distributions, especially in domains tied to stable attributes and values, but remain limited for individual prediction and fail to recover multivariate respondent structure. Retrieval-augmented architectures provide the clearest gains, but performance depends more on human response structure than on model choice: personas perform best for low-variability questions and common respondent patterns, and worst for subjective, heterogeneous, or rare responses. Our results provide practical guidance on when digital personas could be appropriate for survey research and when human validation remains necessary.

Comments:	Accepted to NeurIPS 2026
Subjects:	Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI); Machine Learning (stat.ML)
Cite as:	arXiv:2605.10659 [cs.CL]
	(or arXiv:2605.10659v3 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2605.10659 arXiv-issued DOI via DataCite

Submission history

From: Mumin Jia [view email]
[v1] Mon, 11 May 2026 14:41:11 UTC (6,334 KB)
[v2] Mon, 5 Oct 2026 01:29:11 UTC (6,349 KB)
[v3] Tue, 13 Oct 2026 20:12:03 UTC (6,343 KB)